IMAGE THRESHOLDING OF HISTORICAL DOCUMENTS: APPLICATION TO THE JOAQUIM NABUCO S FILE

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1 IMAGE THRESHOLDING OF HISTORICAL DOCUMENTS: APPLICATION TO THE JOAQUIM NABUCO S FILE Carlos A.B.Mello 1, Ángel Sanchez 2, Adriano L.I.Oliveira 3 Abstract This paper presents a study on thresholding algorithms applied to images of historical documents. Thresholding methods are used to create monochromatic images. The generation of bi-level images of this kind of documents increases the compression rate and make them more easily available for Internet download. We also introduce a new thresholding algorithm which achieves high quality monochromatic images in some extreme situations. 1. Introduction This paper describes some advances achieved in the ProHist Project [25] which aims to define new algorithms for image processing of historical documents. The project is a cooperation between University of Pernambuco, Brazil, and University Rey Juan Carlos, Spain. The aim is to preserve some old documents and to develop different Image Processing tools to make easier the diffusion of historical documents. Currently, the project is being developed using three databases. One of them is composed by documents from the end of the nineteenth century and beginning of the twentieth century. This particular archive contains letters, documents and postcards of Joaquim Nabuco held by the Joaquim Nabuco Foundation. More than 6,500 documents and 30,000 pages are part of this documental patrimony which is analyzed in this work. Joaquim Nabuco (b.1861-d.1910) was one of the most important persons of Brazilian s history as he was statesman, writer, diplomat, and one of the key figures in the campaign for freeing black slaves in Brazil. He was also Brazilian ambassador to London. Fig. 1 presents some sample documents (in greyscale) from the complete file. One of the most difficult situations to deal is shown in Fig. 1-right as we have a document written on both sides of the paper and the ink has transposed from one side to the other provoking a very hard interference. Human visual system can separate both sides but this is not a simple task for computing systems. Fig. 2 presents a zooming into a document with back-to-front interference and the result after the application of a common thresholding algorithm. As one can see, the information is completely lost in the bi-level version. For preservation purposes, the documents are digitized in true colour at 200 dpi resolution and stored in JPEG format with 1% of loss. This was considered by the Joaquim Nabuco Foundation 1 Computing Systems Department, University of Pernambuco, Brazil, cabm@dsc.upe.br 2 ESCET, Rey Juan Carlos University, Madrid, Spain, angel.sanchez@urjc.es 3 Computing Systems Department, University of Pernambuco, Brazil, adriano@dsc.upe.br

2 document specialists a good quality/compression rate relation. Thus, those digitized images are stored in DVD s for preservation means. Even in JPEG, each image occupies in average 400 Kb. Fig. 1. Samples from Joaquim Nabuco s file: (left) a type written document and (right) a handwritten letter. Fig. 2. (left) Zoom into a sample document written on both sides of the paper and (right) its bi-level version generated by an image processing commercial tool with its default settings. For diffusion purposes, one possible solution is the generation of black-and-white images. This decreases the size of the files and also allows the use of specific compression algorithms. Different thresholding algorithms were tested in order to produce high quality monochromatic images. It is the process to convert an image from true colour or greyscale to monochromatic (also called as bilevel). This is the first step in several image processing applications as for Optical Character Recognition (OCR) or for compression schemes. A threshold intensity (or cut-off) value is defined and the colours above it are converted to white while the colours below it are converted to black. Most part of image processing commercial software tools contain thresholding algorithms implemented in it. The major problem is to define the correct cut-off value appropriate in each specific document. It is unacceptable to manually search for this value for each image. Several adaptive algorithms try to solve the thresholding problem. This paper presents an analysis of the use of some well-known thresholding algorithms when applied to different kind of images of historical documents. We also propose a new algorithm that is specifically suited to this type of images. 2. Proposed Thresholding Algorithm There are several algorithms in literature for thresholding purposes [17]. We present here the results of the application of some of them in the document of Fig.1 top-right (the one with back-to-front interference). The algorithms presented are: thresholding by mean grey level [17], percentage of

3 black pixels [17], two peaks [17], iterative selection [19], Pun [18], Kapur et al [6], Johannsen [5], Li-Lee [11], Wu-Lu [22], Renyi [20], Brink [8], Minimum Thresholding [9], Fisher [1], Kittler and Illingworth algorithm [24], Otsu [16], C Means [4], Huang [3], Yager [23], and Ye-Danielsson [2]. Fig. 3 presents the above mentioned algorithms applied to the sample document of Fig. 1-right. It can be observed that the performance of some algorithms was very poor as some images are completely black or white, indicating a very high misclassification rate. Even the best images present noise due to the ink by both sides of the paper. This justifies the need to create a new thresholding algorithm for these specific documents. Fig. 3. Application of thresholding algorithms in the document of Fig. 1-right with back-to-front interference. Our approach considers the entropy feature. Entropy [21] is a measure of the information content. In Information Theory, it is assumed that if there are n possible symbols s i which occur with probabilities p(s i ), the entropy H associated with the source S of symbols is: H( S) = n i= 0 p[ s ]log( p[ i s i where H(S) can be measured in bits/symbols. Although the logarithmic base is not defined, authors in references [7] and [10] analyze that changes in this base do not affect the concept of entropy as it was explored in [13]. First, the new thresholding algorithm scans the image in search for the most frequent greyscale value t. As we are working with images of letters and documents, it is correct to suppose that this colour belongs to the paper. This value is used as an initial threshold for the evaluation of two measures Hb and Hw as: Hb = t i= 0 p[ i]log( p[ i]) and Hw = p[ i]log( p[ i]) (1) where p[i] is the probability of pixel i with colour colour[i] is in the image. This logarithmic base is taken as the area of the image. Hb and Hw can be seen as projections of the entropy H. 255 ]) i= t + 1 Using the values of Hw and Hb, the entropy, H, of the image is evaluated as their sum:

4 H = Hw+ Hb. (2) Based on the value of H, three classes of documents were identified. We define two multiplicative factors, as follows: H 0.25 (documents with few parts of text or very faded ink), then mw = 2 and mb = 3; 0.25 < H < 0.30 (the most common cases), then mw = 1 and mb = 2.6; H 0.30 (documents with many black areas), then mw = mb = 1. These values of mw and mb were found empirically after several experiments where the hit rate of OCR tools [14] in typed documents defined the correct values of the parameters. Next, a threshold value th is defined as: th = mw. Hw + mb. Hb. (3) The image is scanned again and each pixel i with colour greylevel[i] is turned to white if: ( greylevel[ i]/ 256) th. (4) Otherwise, its original colour remains the same (to generate a new greyscale image but with a white background) or it is turned to black (thus generating a bi-level image). This is called the segmentation condition. Fig. 4-left presents the bi-level image generated by the new algorithm for the document showed in Fig. 2-left. One can see that some noise still remains. The threshold value th defined by the proposed entropy-based algorithm is not always the best value. In order to adjust this value, a receiver operating characteristic (ROC) curve from Detection Theory [15] is used. This is generally used in medical analysis or in computer biometrics where some tests can generate true positives (TP), false positives (FP), true negatives (TN) and false negatives (FN) answers. For thresholding applications, these principles can be easily adapted as one can see the TP as the ink pixels correctly classified as object, FP represents background elements classified as object, and so on. A receiver operating characteristic (ROC) curve shows the relationship between probability of detection (PD) and probability of false alarm (PFA) for different threshold values. The probability of detection is the probability of correctly detecting an intruder user. The probability of false alarm is the probability of declaring a user to be an intruder when he/she is a registered user. The detection threshold is systematically changed to examine the performance of the model for different thresholds. The use of different threshold values will produce different classifiers with respective PD and PF parameters. By plotting PD x PFA for different threshold values, we obtain the ROC curves. The new proposed algorithm starts with the application of the previous entropy-based algorithm. The initial threshold value th is used to define a binary matrix M with the same size of the input image. Each cell of this matrix is set to true if the corresponding pixel in the input image IM is equal to th. This produces the PD versus PFA curve (or ROC curve) according to the Algorithm 1. For historical images, the ROC curve defined by this algorithm is a step like function which has its maximum values equal to 1 for both axes. Different threshold values define different ROC curves. Fig. 4-right presents the result of the ROC correction applied to the document of Fig. 2-left. Fig. 5 presents the PD versus PFA curve for the sample image of Fig. 2-left. For this document, we have th = 104 and PFA is equal to 1 when PD is The final image can be seen in Fig. 7-left.

5 Algorithm 1: Computation of PD and PDA vectors. n 1 i=1..nrow j=1..ncol (IM(i,j)=th); //number of true elements in M n 0 i=1..nrow j=1..ncol (IM(i,j)th); //number of false elements in M for t = 0 to 255 PD(t) i=1..nrow j=1..ncol (IM(i,j)>t and M(i,j))/n 1 ; PFA(t) i=1..nrow j=1..ncol (IM(i,j)>t and M(i,j))/n 0 ; end for Fig. 4. Thresholded image of Fig. 2-left generated by the new algorithm (left) without ROC correction and (right) with it. Fig. 5. PD versus PFA curve. For th=104, PD = 0.758, and PFA = 1. As PD is less than 0.9, the value of th must be decreased. It was observed in the handwritten documents that the percentage of ink is about 10% of the complete image area. Therefore, the correct ROC curve must grow to 1 when PD values about 0.9. For this, different values of parameter th must be used. This creates different types of M matrices leading to new PD versus PFA curves. If the curve grows to 1 with PD less than 0.9, then the initial th must decrease; otherwise, it must increase. Fig. 6 presents some resulting images for different th and PD values which turns PFA equals to 1, starting from the initial th=104, and PD= Obviously, the minimum value of th is 0 and its maximum value is Results It is presented in Fig. 7 the result of the application of the algorithm in the document of Fig. 1-right without and with the correction by ROC curves. For a quantitative evaluation of we generated a bilevel image of document of Fig. 1-right where the pixels of the background were turned to white manually. All the noise was eliminated creating a clean image of the document (called reference image). This is the correct final image that we would be looking for with the application of the thresholding algorithms. This allows us to analyze the performance of all the thresholding algorithms tested before (Fig. 3). For the evaluation of the algorithms, we compare pixel-by-pixel the reference image with the images generated by the algorithms, evaluating the values of TP, TN, FP and FN rates. It is common to express these values in terms of precision, recall and accuracy [12]. However, these measures are not suitable for our analysis. They consider that an image with

6 no pixel classified as ink (a completely white image) has a high precision rate as most part of the document is really white (the paper). This makes difficult a correct analysis of the process. A variation defined in [12] consider the measure of precision and recall based on the number of words detected but this makes necessary a manual counting of the words which has a high cost. Fig. 6. Bi-level images generated by different threshold values (th) and the corresponding PD value for which PFA turns equal to 1: (left) th = 104, PD = 0.758, (center-left) th = 90, PD = , (center-right) th = 80, PD = and (right) th = 70, PD = Fig. 7. (left) Document of Fig. 1-left in more details and its bi-level image generated by the new algorithm (center) without (th = 83) and (right) with ROC correction (th = 68). Table 1 lists the values found allowing a better comparison between the algorithms. The images analyzed are the ones presented in Fig. 3. We are looking for the algorithm that achieved the best TP and TN rate (i.e., the algorithm that had a better classification for the ink and the paper). The conclusion is taken based on the average between TP and TN rates (called Average Hit Rate in Table 1). The best average indicates the best algorithm. For example, Johannsen algorithm achieved a TP rate of 100%. But this happened because the image generated was completely black. So the black pixels were all correctly classified. However, the classification of the white pixels (paper) is very low, indicating a very high error rate. The best performance in average was found in our new proposal; it had the higher average hit rate analysing both ink and paper classification. 4. Conclusions This paper introduced an entropy-based thresholding algorithm for images of historical documents. The algorithm defines an initial threshold value which is adjusted by the use of ROC curves. This adjustment generates a new threshold value thus producing better quality bi-level images. The method is quite suitable when applied to documents written on both sides of the paper, presenting back-to-front interference. By visual inspection, the binary images are far better than the ones

7 produced by others well-known algorithms. Using an analysis of the TP and TN rates we can compare the performance of our method with other classical thresholding algorithms. Table 1. Comparison of the algorithms applied to the image of Fig. 1-left using precision, recall and accuracy Algorithm TP rate TN rate FP rate FN rate Average Hit rate Brink 100% 2.98% 0% 97,01% 51.49% Fisher 99.78% 92,65% 0.2% 7.34% 96.25% C Means 99.75% 93.56% 0.25% 6.44% 96.66% Huang 99.78% 92.66% 0.21% 7.33% 96.22% Yager 100% 3.13% 0% 96.86% 51.56% Iterative Selection 99.80% 92.34% 0.20% 7.65% 96.07% Johannsen 100% 2.99% 0% 97.01% 51.50% Kapur 99.85% 90.99% 0.15% 9.01% 90.92% Kittler 0% 100% 100% 0% 50% Li-Lee 100% 2.80% 0% 97.19% 51.40% Mean Gray Level 99.99% 74,03% 0.01% 25.97% 87.01% Minimum Error 99.35% 97.42% 0.65% % Otsu 99.78% 92.66% 0.21% 7.33% 96.22% Percentage of Black 97.96% 98.74% 2.04% 1.26% 98.35% Pun 100% 53.25% 0% 46.75% 76.62% Renyi 100% 48.12% 0% 51.88% 74.06% Two Peaks 100% 39.75% 0% 60.25% 69.88% Wu-Lu 99.51% 96.41% 0.48% 3.59% 97.96% Ye-Danielsson 99.80% 92.34% 0.20% 7.65% 96.07% New Algorithm without ROC 99.52% 96.41% 0.48% 3.58% 97.96% New Algorithm with ROC 99.01% 98.62% 0.98% 1.38% 98.82% The monochromatic images can be used to make files of thousand of historical documents more easily accessible even through mobile devices which have slower connections. The compression rate is about 15:1 when compared the storage of the greyscale image in JPEG and the bi-level version in GIF. As future work, we propose a detailed quantitative analysis of the developed thresholding algorithm using other different digitized historical documents. Due to the achieved compression rates of about 15:1 with this algorithm, an interesting future application of this work would also be to make accessible thousands of historical documents using mobile devices like PDAs. Acknowledgments This research is partially sponsored by CNPq, FACEPE, UPE and Agencia Española de Cooperación Internacional (AECI) contract no. A/2948/ References [1] CHANG, M.S. et al., Improved binarization algorithm for document image by histogram and edge detection, in: Intern. Conf.on Document Analysis and Recognition, pp , Canada, [2] GLASBYE, C.A., An Analysis of Histogram-Based Thresholding Algorithm, in: Graphical Models and Image Processing, 55(6), , Nov [3] HUANG, L.K. and WANG, M.J., Image Thresholding by Minimizing the Measures of Fuzziness, in: Pattern Recognition, 28(1), pp , Jan, 1995.

8 [4] JAWAHAR, C., BISWAS, P. and RAY, K., Investigations on Fuzzy Thresholding Based on Fuzzy Clustering, in: Pattern Recognition, 30(10), pp , Oct, [5] JOHANNSEN, G. and BILLE, J., A Threshold Selection Method using Information Measures, in: Proceedings, 6th Int. Conf. Pattern Recognition, Munich, Germany, pp , [6] KAPUR, J.N. et al. A New Method for Gray-Level Picture Thresholding using the Entropy of the Histogram, in: Computer Vision, Graphics and Image Processing, 29(3), [7] KAPUR, J. N., Measures of Information and their Applications, John Wiley and Sons, [8] KATZ, S.W. and BRINK, A.D., Segmentation of Chromosome Images, in: IEEE COMSIG93, pp 85-90, [9] KITTLER, J. and ILLINGWORTH, J. Minimum Error Thresholding, in: Pattern Recognition, 19(1), pp 41-47, [10] KULLBACK, S. Information Theory and Statistics.Dover Publications, Inc [11] LI, C.H. and LE, C.K., Minimum Cross Entropy Thresholding, in: Pattern Recognition, v.26 (4), pp , [12] TAN, C.L., CAO, R. and SHEN, P. Restoration of archival documents using a wavelet technique, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, pp [13] MELLO, C.A.B. A New Entropy and Logarithmic Based Binarization Algorithm for Grayscale Images, in: IASTED VIIP 2004, Hawaii, USA, [14] MELLO, C.A.B. and LINS, R.D. A Comparative Study on OCR Tools, in: Vision Interface 99, Canada, [15] MCMILLAN, N.A., CREELMAN, C.D. Detection Theory. LEA Publishing, [16] OTSU, N. A threshold selection method from gray-level histogram, in: Trans on Syst., Man, and Cybernetics, vol 8, 62-66, [17] PARKER, J.R. Algorithms for Image Processing and Computer Vision, John Wiley and Sons, [18] PUN, T., Entropic Thresholding, A New Approach, in: C.Graphics and Image Proc., vol. 16, pp , [19] RIDLER, T.W. and CALVARD, S., Picture Thresholding Using an Iterative Selection Method, in: IEEE Trans. on Systems, Man and Cybernetics, Vol.SMC-8, 8: , 1978 [20] SAHOO, P. et al. Threshold Selection using Renyi s Entropy, in: Pattern recognition V. 30, No 1, pp 71-84, 1997 [21] SHANNON, C., A Mathematical Theory of Communication, in: Bell System Technology Journal, v. 27, pp , , [22] WU, L., MA, S., LU, H., An Effective Entropic thresholding for Ultrasonic Images, in: ICPR98, pp , [23] YAGER, R.R., On the Measures of Fuzziness and Negation.Part.1: Membership in the Unit Interval, in: Int Journal of General Systems, Vol. 5, pp , [24] YAN, H. Unified Formulation of a Class of Image Thresholding Techniques, Pattern Recognition, V. 29, pp , [25] PROHIST PROJECT: